We further apply our approach for sound-guided artistic painting manipulation. We train our StyleGAN2 [1]}-based generator with a fine-art paintings dataset, called WikiArt [2]}, which contains over 50k images of paintings from 195 different artists. We observe in Fig. REF  that our model successfully produces manipulated images for art paintings guided by audio inputs (e.g., laughing, crying, bomb, raining, or bird). For example, given a laughing sound as a semantic cue, a portrait painting of a woman is manipulated (see 1st column), but the existing approach by Lee et al. does not. Our human study (done in the same setting as our user study) also confirms that 61.2% of 50 participants judge that our results are better manipulated than those by Lee et al. (26.7%) and text-based approach (12.1%).
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